title: Macromolecular symmetric assembly prediction using swarm intelligence dynamic modeling.
abstract: Proteins often assemble in multimeric complexes to perform a specific biologic function. However, trapping these high-order conformations is difficult experimentally. Therefore, predicting how proteins assemble using in silico techniques can be of great help. The size of the associated conformational space and the fact that proteins are intrinsically flexible structures make this optimization problem extremely challenging. Nonetheless, known experimental spatial restraints can guide the search process, contributing to model biologically relevant states. We present here a swarm intelligence optimization protocol able to predict the arrangement of protein symmetric assemblies by exploiting a limited amount of experimental restraints and steric interactions. Importantly, within this scheme the native flexibility of each protein subunit is taken into account as extracted from molecular dynamics (MD) simulations. We show that this is a key ingredient for the prediction of biologically functional assemblies when, upon oligomerization, subunits explore activated states undergoing significant conformational changes.
authors.affiliation: Institute of Bioengineering, School of Life Sciences, Ecole Polytechnique Fe´ de´ rale de Lausanne - EPFL, CH-1025 Lausanne.
authors.forename: Matteo T
authors.initials: MT
authors.lastname: Degiacomi
authors.forename: Matteo
authors.initials: M
authors.lastname: Dal Peraro
mesh_terms: D000465:Algorithms; D006801:Humans; D056004:Molecular Dynamics Simulation; D046912:Multiprotein Complexes / Q000737:chemistry*; D020836:Protein Structure, Quaternary; D017433:Protein Structure, Secondary; D017434:Protein Structure, Tertiary; D021122:Protein Subunits / Q000737:chemistry
chemical_list: D046912:Multiprotein Complexes; D021122:Protein Subunits